Das Verstandnis des einstigen Modewortes "E-Commerce" hat sich verschoben. Nicht langer stehen vage Prognosen im Mittelpunkt. Der vorliegende Band unterzieht die Potenziale des Technologieeinsatzes und ihrer nachhaltigen oekonomischen Verwertung einer realistischen Analyse. Namhafte Wissenschaftler und Praktiker geben einen UEberblick uber die aktuelle Forschung sowie Anwendungen in den Bereichen Netze, Markte, Dienste und Technologien. Dabei werden die Moeglichkeiten der Umsetzung innovativer wissenschaftlicher Ansatze in die Praxis, aber auch des Transfers praxisrelevanter Problemstellungen in die Forschungslabors sowohl aus oekonomischer als auch aus informationstechnischer Sicht beleuchtet.

Monte Carlo statistical methods, particularly those based on Markov chains, are now an essential component of the standard set of techniques used by statisticians. This new edition has been revised towards a coherent and flowing coverage of these simulation techniques, with incorporation of the most recent developments in the field. In particular, the introductory coverage of random variable generation has been totally revised, with many concepts being unified through a fundamental theorem of simulation

There are five completely new chapters that cover Monte Carlo control, reversible jump, slice sampling, sequential Monte Carlo, and perfect sampling. There is a more in-depth coverage of Gibbs sampling, which is now contained in three consecutive chapters. The development of Gibbs sampling starts with slice sampling and its connection with the fundamental theorem of simulation, and builds up to two-stage Gibbs sampling and its theoretical properties. A third chapter covers the multi-stage Gibbs sampler and its variety of applications. Lastly, chapters from the previous edition have been revised towards easier access, with the examples getting more detailed coverage.

This textbook is intended for a second year graduate course, but will also be useful to someone who either wants to apply simulation techniques for the resolution of practical problems or wishes to grasp the fundamental principles behind those methods. The authors do not assume familiarity with Monte Carlo techniques (such as random variable generation), with computer programming, or with any Markov chain theory (the necessary concepts are developed in Chapter 6). A solutions manual, which coversapproximately 40% of the problems, is available for instructors who require the book for a course.

Christian P. Robert is Professor of Statistics in the Applied Mathematics Department at UniversitA(c) Paris Dauphine, France. He is also Head of the Statistics Laboratory at the Center for Research in Economics and Statistics (CREST) of the National Institute for Statistics and Economic Studies (INSEE) in Paris, and Adjunct Professor at Ecole Polytechnique. He has written three other books, including The Bayesian Choice, Second Edition, Springer 2001. He also edited Discretization and MCMC Convergence Assessment, Springer 1998. He has served as associate editor for the Annals of Statistics and the Journal of the American Statistical Association. He is a fellow of the Institute of Mathematical Statistics, and a winner of the Young Statistician Award of the SocietiA(c) de Statistique de Paris in 1995.

George Casella is Distinguished Professor and Chair, Department of Statistics, University of Florida. He has served as the Theory and Methods Editor of the Journal of the American Statistical Association and Executive Editor of Statistical Science. He has authored three other textbooks: Statistical Inference, Second Edition, 2001, with Roger L. Berger; Theory of Point Estimation, 1998, with Erich Lehmann; and Variance Components, 1992, with Shayle R. Searle and Charles E. McCulloch. He is a fellow of the Institute of Mathematical Statistics and the American Statistical Association, and an elected fellow of the International Statistical Institute.

Presents the main ideas of computer-intensive statistical methods Gives the algorithms for all the methods Uses various plots and illustrations for explaining the main ideas Features the theoretical backgrounds of the main methods. Includes R codes for the methods and examples

This book presents two new decomposition methods to decompose a time series in intrinsic components of low and high frequencies. The methods are based on Singular Value Decomposition (SVD) of a Hankel matrix (HSVD). The proposed decomposition is used to improve the accuracy of linear and nonlinear auto-regressive models. Linear Auto-regressive models (AR, ARMA and ARIMA) and Auto-regressive Neural Networks (ANNs) have been found insufficient because of the highly complicated nature of some time series. Hybrid models are a recent solution to deal with non-stationary processes which combine pre-processing techniques with conventional forecasters, some pre-processing techniques broadly implemented are Singular Spectrum Analysis (SSA) and Stationary Wavelet Transform (SWT). Although the flexibility of SSA and SWT allows their usage in a wide range of forecast problems, there is a lack of standard methods to select their parameters. The proposed decomposition HSVD and Multilevel SVD are described in detail through time series coming from the transport and fishery sectors. Further, for comparison purposes, it is evaluated the forecast accuracy reached by SSA and SWT, both jointly with AR-based models and ANNs.

Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today's most popular machine learning methods. This book serves as a practitioner's guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, keras, and others to effectively model and gain insight from their data. The book favors a hands-on approach, providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory. Throughout this book, the reader will be exposed to the entire machine learning process including feature engineering, resampling, hyperparameter tuning, model evaluation, and interpretation. The reader will be exposed to powerful algorithms such as regularized regression, random forests, gradient boosting machines, deep learning, generalized low rank models, and more! By favoring a hands-on approach and using real word data, the reader will gain an intuitive understanding of the architectures and engines that drive these algorithms and packages, understand when and how to tune the various hyperparameters, and be able to interpret model results. By the end of this book, the reader should have a firm grasp of R's machine learning stack and be able to implement a systematic approach for producing high quality modeling results. Features: * Offers a practical and applied introduction to the most popular machine learning methods. * Topics covered include feature engineering, resampling, deep learning and more. * Uses a hands-on approach and real world data.

Die Evolution grosser Software-Systeme halt fur viele Unternehmen immer wieder UEberraschungen bereit. Software-Konfigurationsmanagement dient dazu, Zeit und Aufwand bei der Entwicklung und Pflege langlebiger komplexer Softwaresysteme zu reduzieren und die Software-Evolution beherrschbar zu machen. Das Buch beschreibt die Einfuhrung und effiziente Anwendung von Konfigurationsmanagement und stellt die Integration in das AEnderungsmanagement ausfuhrlich dar.

This textbook on practical data analytics unites fundamental principles, algorithms, and data. Algorithms are the keystone of data analytics and the focal point of this textbook. Clear and intuitive explanations of the mathematical and statistical foundations make the algorithms transparent. But practical data analytics requires more than just the foundations. Problems and data are enormously variable and only the most elementary of algorithms can be used without modification. Programming fluency and experience with real and challenging data is indispensable and so the reader is immersed in Python and R and real data analysis. By the end of the book, the reader will have gained the ability to adapt algorithms to new problems and carry out innovative analyses. This book has three parts:(a) Data Reduction: Begins with the concepts of data reduction, data maps, and information extraction. The second chapter introduces associative statistics, the mathematical foundation of scalable algorithms and distributed computing. Practical aspects of distributed computing is the subject of the Hadoop and MapReduce chapter.(b) Extracting Information from Data: Linear regression and data visualization are the principal topics of Part II. The authors dedicate a chapter to the critical domain of Healthcare Analytics for an extended example of practical data analytics. The algorithms and analytics will be of much interest to practitioners interested in utilizing the large and unwieldly data sets of the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System.(c) Predictive Analytics Two foundational and widely used algorithms, k-nearest neighbors and naive Bayes, are developed in detail. A chapter is dedicated to forecasting. The last chapter focuses on streaming data and uses publicly accessible data streams originating from the Twitter API and the NASDAQ stock market in the tutorials. This book is intended for a one- or two-semester course in data analytics for upper-division undergraduate and graduate students in mathematics, statistics, and computer science. The prerequisites are kept low, and students with one or two courses in probability or statistics, an exposure to vectors and matrices, and a programming course will have no difficulty. The core material of every chapter is accessible to all with these prerequisites. The chapters often expand at the close with innovations of interest to practitioners of data science. Each chapter includes exercises of varying levels of difficulty. The text is eminently suitable for self-study and an exceptional resource for practitioners.

Master the application of artificial intelligence in your enterprise with the book series trusted by millions In Enterprise AI For Dummies, author Zachary Jarvinen simplifies and explains to readers the complicated world of artificial intelligence for business. Using practical examples, concrete applications, and straightforward prose, the author breaks down the fundamental and advanced topics that form the core of business AI. Written for executives, managers, employees, consultants, and students with an interest in the business applications of artificial intelligence, Enterprise AI For Dummies demystifies the sometimes confusing topic of artificial intelligence. No longer will you lag behind your colleagues and friends when discussing the benefits of AI and business. The book includes discussions of AI applications, including: Streamlining business operations Improving decision making Increasing automation Maximizing revenue The For Dummies series makes topics understandable, and as such, this book is written in an easily understood style that's perfect for anyone who seeks an introduction to a usually unforgiving topic.

"This book is a great way to both start learning data science through the promising Julia language and to become an efficient data scientist."- Professor Charles Bouveyron, INRIA Chair in Data Science, Universite Cote d'Azur, Nice, France Julia, an open-source programming language, was created to be as easy to use as languages such as R and Python while also as fast as C and Fortran. An accessible, intuitive, and highly efficient base language with speed that exceeds R and Python, makes Julia a formidable language for data science. Using well known data science methods that will motivate the reader, Data Science with Julia will get readers up to speed on key features of the Julia language and illustrate its facilities for data science and machine learning work. Features: Covers the core components of Julia as well as packages relevant to the input, manipulation and representation of data. Discusses several important topics in data science including supervised and unsupervised learning. Reviews data visualization using the Gadfly package, which was designed to emulate the very popular ggplot2 package in R. Readers will learn how to make many common plots and how to visualize model results. Presents how to optimize Julia code for performance. Will be an ideal source for people who already know R and want to learn how to use Julia (though no previous knowledge of R or any other programming language is required). The advantages of Julia for data science cannot be understated. Besides speed and ease of use, there are already over 1,900 packages available and Julia can interface (either directly or through packages) with libraries written in R, Python, Matlab, C, C++ or Fortran. The book is for senior undergraduates, beginning graduate students, or practicing data scientists who want to learn how to use Julia for data science. "This book is a great way to both start learning data science through the promising Julia language and to become an efficient data scientist." Professor Charles Bouveyron INRIA Chair in Data Science Universite Cote d'Azur, Nice, France

Der enorme Kostendruck in Industrieunternehmen sowie der erkennbare Wandel der Wertschopfungsketten hin zu Wertschopfungsnetzwerken werden die Bedeutung der Beschaffung auf den Unternehmenserfolg sowie die Komplexitat der Beschaffungsaufgaben noch weiter erhohen. Diese Herausforderung kann nur durch den verstarkten Einsatz geeigneter, prozessorientierter Informationstechnologie bei der Beschaffung direkter Guter bewaltigt werden.

Dieses Buch bietet durch die Darstellung des State-of-the-Art und der Entwicklungstendenzen aus Sicht der Wissenschaft sowie namhafter IT-Anbieter-, Beratungs- und Industrieunternehmen erstmals einen ganzheitlichen Uberblick uber Strategien, Prozesse und Systeme bei der Beschaffung direkter Guter. Daraus konnen Handlungsempfehlungen fur die konkrete Ausgestaltung in den Unternehmen gewonnen werden."

There's a lot more to the blockchain than mining Bitcoin. This secure system for registering and verifying ownership and identity is perfect for supply chain logistics, health records, and other sensitive data management tasks. Blockchain in Action unlocks the full potential of this revolutionary technology, showing you how to build own decentralized apps for secure applications including digital democracy, private auctions, and electronic record management. Key Features * How blockchain differs from other distributed systems * Smart contract development with Ethereum and the Solidity language * Web UI for decentralized apps * Identity, privacy and security techniques * On-chain and off-chain data storage For intermediate programmers who know the basics of object-oriented languages and have a working knowledge of JavaScript. About the technology A blockchain is a decentralized record, stored across numerous devices with no central control or authority. Copies of this shared database are constantly reconciled with one another, and records are cryptographically encoded to make them unchangeable. The result is a type of database that is at once transparent and publicly accessible, and where it is impossible to falsify or alter the historic data record. Bina Ramamurthy holds a Ph.D. in fault-tolerant distributed systems, and has thirty years of experience teaching cryptography, peer-to-peer networking, and distributed systems. She is the instructor and content creator for the University of Buffalo four-course specialization on blockchain technology on the Coursera MOOC platform, and the recipient of the 2019 SUNY Chancellor's Award for Teaching Excellence.

E(lectronic)- und M(obile)-Learning: das Lernen und Lehren mittels Informations- und Kommunikationstechnologien wird bereits in vielen Bereichen erfolgreich eingesetzt. In (Hoch)schulen sowie in der beruflichen Aus-, Fort- und Weiterbildung von Auszubildenden bis hin zu Top-Managern. Dieser Sammelband beschreibt den Status Quo und aktuelle Projekte. Er identifiziert und analysiert wichtige E-Learning-Trends und zukunftsgerichtete Entwicklungen.

Reproducible Finance with R: Code Flows and Shiny Apps for Portfolio Analysis is a unique introduction to data science for investment management that explores the three major R/finance coding paradigms, emphasizes data visualization, and explains how to build a cohesive suite of functioning Shiny applications. The full source code, asset price data and live Shiny applications are available at reproduciblefinance.com. The ideal reader works in finance or wants to work in finance and has a desire to learn R code and Shiny through simple, yet practical real-world examples. The book begins with the first step in data science: importing and wrangling data, which in the investment context means importing asset prices, converting to returns, and constructing a portfolio. The next section covers risk and tackles descriptive statistics such as standard deviation, skewness, kurtosis, and their rolling histories. The third section focuses on portfolio theory, analyzing the Sharpe Ratio, CAPM, and Fama French models. The book concludes with applications for finding individual asset contribution to risk and for running Monte Carlo simulations. For each of these tasks, the three major coding paradigms are explored and the work is wrapped into interactive Shiny dashboards.

Compositional Data Analysis in Practice is a user-oriented practical guide to the analysis of data with the property of a constant sum, for example percentages adding up to 100%. Compositional data can give misleading results if regular statistical methods are applied, and are best analysed by first transforming them to logarithms of ratios. This book explains how this transformation affects the analysis, results and interpretation of this very special type of data. All aspects of compositional data analysis are considered: visualization, modelling, dimension-reduction, clustering and variable selection, with many examples in the fields of food science, archaeology, sociology and biochemistry, and a final chapter containing a complete case study using fatty acid compositions in ecology. The applicability of these methods extends to other fields such as linguistics, geochemistry, marketing, economics and finance. R Software The following repository contains data files and R scripts from the book https://github.com/michaelgreenacre/CODAinPractice. The R package easyCODA, which accompanies this book, is available on CRAN -- note that you should have version 0.25 or higher. The latest version of the package will always be available on R-Forge and can be installed from R with this instruction: install.packages("easyCODA", repos="http://R-Forge.R-project.org").

Sufficient dimension reduction is a rapidly developing research field that has wide applications in regression diagnostics, data visualization, machine learning, genomics, image processing, pattern recognition, and medicine, because they are fields that produce large datasets with a large number of variables. Sufficient Dimension Reduction: Methods and Applications with R introduces the basic theories and the main methodologies, provides practical and easy-to-use algorithms and computer codes to implement these methodologies, and surveys the recent advances at the frontiers of this field. Features Provides comprehensive coverage of this emerging research field. Synthesizes a wide variety of dimension reduction methods under a few unifying principles such as projection in Hilbert spaces, kernel mapping, and von Mises expansion. Reflects most recent advances such as nonlinear sufficient dimension reduction, dimension folding for tensorial data, as well as sufficient dimension reduction for functional data. Includes a set of computer codes written in R that are easily implemented by the readers. Uses real data sets available online to illustrate the usage and power of the described methods. Sufficient dimension reduction has undergone momentous development in recent years, partly due to the increased demands for techniques to process high-dimensional data, a hallmark of our age of Big Data. This book will serve as the perfect entry into the field for the beginning researchers or a handy reference for the advanced ones. The author Bing Li obtained his Ph.D. from the University of Chicago. He is currently a Professor of Statistics at the Pennsylvania State University. His research interests cover sufficient dimension reduction, statistical graphical models, functional data analysis, machine learning, estimating equations and quasilikelihood, and robust statistics. He is a fellow of the Institute of Mathematical Statistics and the American Statistical Association. He is an Associate Editor for The Annals of Statistics and the Journal of the American Statistical Association.

Erfolgreiche Veranderung hangt von der zielgerichteten Umsetzung pragmatischer Konzepte ab. Das Business Engineering liefert diese Konzepte. Das Buch zeigt, wie sie in der betrieblichen Realitat zu erfolgreichen Projekten fuhren. Die Nutzung der Informationstechnologie ist dabei das verbindende Element. Die von erfahrenen Praktikern des Business Engineering verfassten Beitrage drehen sich zum einen um technologiegetriebene Wertschopfungspotenziale und zum anderen um den methodischen Transformationsprozess zum Unternehmen des Informationszeitalters. Sie beschaftigen sich mit den zentralen Fragen des unternehmerischen Wandels: Wie andert sich die Geschaftslogik z.B. von Finanzdienstleistern, Industrieunternehmen oder Immobilienmanagement-Gesellschaften unterstutzt durch innovative Anwendungen? Welche Potenziale ergeben sich fur Supply-Chain-Management-Prozesse oder fur ein innovatives HR-Management? Welche Effekte ergeben sich in Netzwerken? Wie lassen sich die Erkenntnisse in KMU anwenden?

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This book discusses all major topics on survey sampling and estimation. It covers traditional as well as advanced sampling methods related to the spatial populations. The book presents real-world applications of major sampling methods and illustrates them with the R software. As a large sample size is not cost-efficient, this book introduces a new method by using the domain knowledge of the negative correlation between the variable of interest and the auxiliary variable in order to control the size of a sample. In addition, the book focuses on adaptive cluster sampling, rank-set sampling and their applications in real life. Advance methods discussed in the book have tremendous applications in ecology, environmental science, health science, forestry, bio-sciences, and humanities. This book is targeted as a text for undergraduate and graduate students of statistics, as well as researchers in various disciplines.

After the fundamental volume and the advanced technique volume, this volume focuses on R applications in the quantitative investment area. Quantitative investment has been hot for some years, and there are more and more startups working on it, combined with many other internet communities and business models. R is widely used in this area, and can be a very powerful tool. The author introduces R applications with cases from his own startup, covering topics like portfolio optimization and risk management.

Der Einsatz von Software-Agenten zur Koordination wirtschaftlicher Prozesse und auf elektronischen Marktpl tzen ist Kernthema dieses Buches. Dabei werden Potenziale und Chancen, Anwendungen und Prototypen, aber auch Herausforderungen, Grenzen und Risiken der Agententechnologie f r den Einsatz aufgezeigt. Theoretische Grundlagen und Beispiele aus Projekten und deren Konzepte dienen als Basis f r die Realisierung eines eigenen agentenbasierten Markplatzes in Java. Im Vordergrund stehen daher praktische Ans tze zur Realisierung wirtschaftlicher Mechanismen und deren Implementierungen in eigenen Software-Agenten.
Die beigef gte CD enth lt eine eingeschr nkte Lizenzversion des kommerziellen Agentensystems "Living Agents Runtime System" sowie die im Buch beschriebenen Java-Klassen.

The Workflow of Data Analysis Using Stata, by J. Scott Long, is an essential productivity tool for data analysts. Long presents lessons gained from his experience and demonstrates how to design and implement efficient workflows for both one-person projects and team projects. After introducing workflows and explaining how a better workflow can make it easier to work with data, Long describes planning, organizing, and documenting your work. He then introduces how to write and debug Stata do-files and how to use local and global macros. After a discussion of conventions that greatly simplify data analysis the author covers cleaning, analyzing, and protecting data.

This book shows the capabilities of Microsoft Excel in teaching marketing statistics effectively. It is a step-by-step, exercise-driven guide for students and practitioners who need to master Excel to solve practical marketing problems. If understanding statistics isn't your strongest suit, you are not especially mathematically inclined, or if you are wary of computers, this is the right book for you.Excel, a widely available computer program for students and managers, is also an effective teaching and learning tool for quantitative analyses in marketing courses. Its powerful computational ability and graphical functions make learning statistics much easier than in years past. Excel 2019 for Marketing Statistics: A Guide to Solving Practical Problems capitalizes on these improvements by teaching students and managers how to apply Excel to statistical techniques necessary in their courses and work. In this new edition, each chapter explains statistical formulas and directs the reader to use Excel commands to solve specific, easy-to-understand marketing problems. Practice problems are provided at the end of each chapter with their solutions in an appendix. Separately, there is a full practice test (with answers in an appendix) that allows readers to test what they have learned.

It's no secret that cloud-based computing is the next big movement in IT, and Microsoft is right there in the market with Office 365a cloud-based productivity suite which includes a hosted, cloud-focused version of SharePoint 2010 SharePoint 2010 developers who have traditionally developed for on-premise environments will suddenly find themselves being asked to develop for the cloud. While there is a lot of overlap between cloud-based and traditional SharePoint development, there are also some important differences and considerations that must be taken into account as well. In particular, the proliferation of cloud-based solutions was a driving force behind certain new features in SharePoint 2010, like sandboxed solutions and the new client object model. As the devil is always in the details, Pro SharePoint 2010 Development for Office 365 helps you navigate the changes and develop compelling applications and solutions for SharePoint Online in Office 365. Authors Dave Milner, Bart McDonough, and Paul Stork bring to the table decades of experience in real-world development of solutions for customersexpertise that is the practical result of what works in real-world customer environments. This proven team will cover with you the architectural landscape that SharePoint in the cloud represents, discuss the steps in setting up a development environment, and cover multiple real-world development approaches, technologies, and considerations. What you'll learn Explicit advice for setting up development environments to work with Office 365 Coverage of possibilities for development including browser, SharePoint Designer, and Visual Studio Real-world development approaches In-depth coverage of sandboxed solutions including specific Office 365 considerations How to integrate InfoPath into an Office 365 SharePoint Online environment How to develop and deploy Silverlight applications within SharePoint Online Instructions for incorporating the most popular web development language JavaScriptand the most popular add-onjQuery Instructions for working with HTML5 and CSS3 with SharePoint Online Who this book is for

Online developers will findPro SharePoint 2010 Development for Office 365 most useful. Developers for SharePoint and .NET developers interested in SharePoint solutions for Office 365 will greatly benefit from a clear approach and road map to get into developing for SharePoint in an Office 365 environment. Online developers without a background in SharePoint will also greatly benefit from a concise approach to focusing on necessary concepts and components to get up to speed quickly in developing solutions for SharePoint Office 365. Table of Contents Getting Started with Office 365 and SharePoint Online SharePoint Online Development Overview Setting Up a Development Environment for SharePoint Online Basic Customization Using Only a Browser Taking It to the Next Level with SharePoint Designer InfoPath Forms and SharePoint Online Custom Development with Visual Studio SharePoint Designer Intro to Client-Side Development Client-Side Development with Silverlight Developing with jQuery, HTML5, and CSS3 Hybrid On-Premise/Online Solutions Office 365 Preview (Office 2013)

This book provides a complete and comprehensive guide to Pyomo (Python Optimization Modeling Objects) for beginning and advanced modelers, including students at the undergraduate and graduate levels, academic researchers, and practitioners. Using many examples to illustrate the different techniques useful for formulating models, this text beautifully elucidates the breadth of modeling capabilities that are supported by Pyomo and its handling of complex real-world applications. In the third edition, much of the material has been reorganized, new examples have been added, and a new chapter has been added describing how modelers can improve the performance of their models. The authors have also modified their recommended method for importing Pyomo. A big change in this edition is the emphasis of concrete models, which provide fewer restrictions on the specification and use of Pyomo models. Pyomo is an open source software package for formulating and solving large-scale optimization problems. The software extends the modeling approach supported by modern AML (Algebraic Modeling Language) tools. Pyomo is a flexible, extensible, and portable AML that is embedded in Python, a full-featured scripting language. Python is a powerful and dynamic programming language that has a very clear, readable syntax and intuitive object orientation. Pyomo includes Python classes for defining sparse sets, parameters, and variables, which can be used to formulate algebraic expressions that define objectives and constraints. Moreover, Pyomo can be used from a command-line interface and within Python's interactive command environment, which makes it easy to create Pyomo models, apply a variety of optimizers, and examine solutions.

Beginning R: An Introduction to Statistical Programming is a hands-on book showing how to use the R language, write and save R scripts, build and import data files, and write your own custom statistical functions. R is a powerful open-source implementation of the statistical language S, which was developed by AT&T. R has eclipsed S and the commercially-available S-Plus language, and has become the de facto standard for doing, teaching, and learning computational statistics. R is both an object-oriented language and a functional language that is easy to learn, easy to use, and completely free. A large community of dedicated R users and programmers provides an excellent source of R code, functions, and data sets. R is also becoming adopted into commercial tools such as Oracle Database. Your investment in learning R is sure to pay off in the long term as R continues to grow into the go to language for statistical exploration and research.* Covers the freely-available R language for statistics * Shows the use of R in specific uses case such as simulations, discrete probability solutions, one-way ANOVA analysis, and more * Takes a hands-on and example-based approach incorporating best practices with clear explanations of the statistics being done What you'll learn * Acquire and install R * Import and export data and scripts * Generate basic statistics and graphics * Program in R to write custom functions * Use R for interactive statistical explorations * Implement simulations and other advanced techniques Who this book is for Beginning R: An Introduction to Statistical Programming is an easy-to-read book that serves as an instruction manual and reference for working professionals, professors, and students who want to learn and use R for basic statistics. It is the perfect book for anyone needing a free, capable, and powerful tool for exploring statistics and automating their use.

You think agile techniques might be for you, but your projects and organization are unique. An "out-of-the-box" agile approach won't work. Instead, unite agile and lean principles for your project. See how to design a custom approach, reap the benefits of collaboration, and deliver value. For project managers who want to use agile techniques, managers who want to start, and technical leaders who want to know more and succeed, this book is your first step toward agile project success. You've tried to use an off-the-shelf approach to agile techniques, and it's not working. Instead of a standard method or framework, work from agile and lean principles to design your own agile approach in a way that works for you. Build collaborative, cross-functional teams. See how small batch sizes and frequent delivery create an environment of trust and transparency between the team, management, and customers. Learn about the interpersonal skills that help agile teams work together so well. In addition to seeing work and knowing what "done" means, you'll see examples of many possible team-based measurements. Look at tools you can use for status reporting, and how to use those measurements to help your managers understand what agile techniques buy them. Recognize the traps that prevent agile principles from working in too many organizations, and what to do about those traps. Use agile techniques for workgroups, and see what managers can do to create and nurture an agile culture. You might be surprised at how few meetings and rituals you need to still work in an agile way. Johanna's signature frankness and humor will get you on the right track to design your agile project to succeed. What You Need: No technical expertise or experience needed, just a desire to know more about how you might use agile in your project.